Echo canceller and a method thereof

ABSTRACT

The present invention relates to an echo canceller ( 300 ) for estimating a model of an echo signal and a method thereof. The echo canceller comprises at least an adaptive main filter ( 301 ) for modeling the echo signal and an adaptive shadow filter ( 302 ) for modeling the echo signal. The adaptive main filter is an adaptive filter for which the adaptation speed is proportional to a system noise estimate and the adaptation speed of the shadow filter ( 302 ) is faster than the adaptation speed of the adaptive main filter ( 301 ). The echo canceller ( 300 ) comprises a processor ( 303 ) for determining whether the adaptive shadow filter models the echo signal better than the adaptive main filter and an updater ( 304 ) for updating the system noise estimate of the adaptive main filter ( 301 ) if the adaptive shadow filter models ( 302 ) the echo signal better than the adaptive main filter ( 301 ).

TECHNICAL FIELD

The present invention relates to an echo canceller and a method thereof,and in particular to an improvement of the echo estimation.

BACKGROUND

Echo cancellers improve the voice quality in a voice communication byremoving the echo from the voice communication. Such an echo cancelleris exemplified in FIG. 1. x(t) is the speaker signal, y(t) is themicrophone signal, s(t) is the echo from the speakers, v(t) is thenear-end signal and H(z) represents adaptive filters.

Echo cancellers are based on estimating a model of the echo signal,usually implemented as an FIR (Finite Impulse Response) filter. Due tothe time variations in the echo path, the model needs to be continuouslyadapted.

A Kalman filter is an example of a FIR filter which can be used forimplementing the linear filter adaptation. The Kalman filter is given by

h(t + 1) = h(t) + K(t) ⋅ (y(t) − X^(T)(t)h(t))${K(t)} = \frac{{P(t)}{X(t)}}{{R_{2}(t)} + {{X^{T}(t)}{P(t)}{X(t)}}}$P(t + 1) = P(t) + R₁(t) − K(t)X^(T)(t)P(t)

The computation of the updating gain in the Kalman filter includes thecovariance matrix P(t) that has a dimension equal to the filter order.Hence, the computational load of the Kalman filter is in many situationsprohibitively large for real-time implementation of echo cancellation.

Several approximations of the Kalman filter have been proposed to reducethe computational load and in particular the Normalized Least MeanSquares method for updating FIR filters with adaptive step-sizecomputations can be viewed as an approximate Kalman filterimplementation.

For speech signals, it is advantageous to implement the NLMS in thefrequency domain, particularly for high sample rates. In order to handlelong impulse responses, a partitioned block structure where the impulseresponse of the echo path is segmented into n_(p) consecutive blocks oflength P coefficients is used,

${H_{echo}(z)} = {{\sum\limits_{n = 0}^{{({n_{P} - 1})} \cdot P}\; {h_{n} \cdot z^{- n}}} = {\sum\limits_{p = 1}^{n_{P}}\; {\sum\limits_{n = 0}^{P - 1}\; {h_{p,n} \cdot z^{{{- {({p - 1})}} \cdot P} - n}}}}}$${{H_{p}(f)} = {{FFT}\left( \begin{bmatrix}h_{p,0} \\M \\h_{p,{P - 1}} \\0_{M - P}\end{bmatrix} \right)}},{p = 1},K,n_{P}$${{X_{p}\left( {k,f} \right)} = {{FFT}\left( \begin{bmatrix}{x\left( {{k \cdot L} - {\left( {p - 1} \right) \cdot P} - M + 1} \right)} \\M \\{x\left( {{k \cdot L} - {\left( {p - 1} \right) \cdot P}} \right)}\end{bmatrix} \right)}},{p = 1},K,n_{P}$${S(f)} = {\sum\limits_{p = 1}^{n_{P}}\; {{H_{p}(f)} \cdot {X_{p}(f)}}}$$\left( \begin{bmatrix}\rho \\0_{M - L} \\{s\left( {{k \cdot L} - 1} \right)} \\M \\{s\left( {k \cdot L} \right)}\end{bmatrix} \right) = {{IFFT}\left( {S(f)} \right)}$

where x(t) is the loudspeaker signal, s(t) is the echo, L is the dataframe length, and M is the FFT length.

Each partition is recursively estimated as

  H_(p)(k, f) = H_(p)(k − 1, f) + α(k, f) ⋅ (E(k, f) ⋅ X_(p)^(*)(k, f)),   p = 1, K, n_(P)  where ${E\left( {k,f} \right)} = {{{FFT}\left( \begin{bmatrix}\rho \\0_{M - L} \\{e\left( {{k \cdot L} - 1} \right)} \\M \\{e\left( {k \cdot L} \right)}\end{bmatrix} \right)} = {{{FFT}\left( \begin{bmatrix}\rho \\0_{M - L} \\{y\left( {{k \cdot L} - 1} \right)} \\M \\{y\left( {k \cdot L} \right)}\end{bmatrix} \right)} - {{FFT}\left( \begin{bmatrix}\rho \\0_{M - L} \\{s\left( {{k \cdot L} - 1} \right)} \\M \\{s\left( {k \cdot L} \right)}\end{bmatrix} \right)}}}$

where y(t) is the microphone signal and α(k,f) is the update gainfactor.

From an analysis of the expected variance of the estimation error of thefilter coefficients, i.e. |H_(m)(k,f)−H₀(k,f)|² where H₀ (k,f) denotesthe filter coefficients of a linear filter modeling the echo, anadaptive updating gain that minimizes the expected estimation error maybe implemented by choosing α(k,f) as κ(k,f) updated by

${\kappa \left( {k,f} \right)} = \frac{R_{H}\left( {{k - 1},f} \right)}{{n_{P}{\frac{P}{M} \cdot {R_{X}\left( {k,f} \right)} \cdot {R_{H}\left( {{k - 1},f} \right)}}} + {n_{P}\frac{P}{L}{R_{V}\left( {k,f} \right)}}}$${R_{H}\left( {k,f} \right)} = {{\left( {1 - {\frac{L}{M} \cdot {\kappa \left( {k,f} \right)} \cdot {R_{X}\left( {k,f} \right)}}} \right) \cdot {R_{H}\left( {k - 1} \right)}} + {Q\left( {k,f} \right)}}$where${{R_{X}\left( {k,f} \right)} = {\frac{1}{n_{P}}{\sum\limits_{p = 1}^{n_{P}}\; {{X_{p}\left( {k,f} \right)}}^{2}}}},{{R_{V}\left( {k,f} \right)} = {{{E\left( {k,f} \right.}^{2} \approx {{V\left( {k,f} \right.}^{2}}}}}$

Comparing this to the expression for the regular Kalman filter givesthat

R_(H)(k,f) corresponds to the diagonal elements of P(t)

Q(k,f) corresponds to R₁(t) (the variance of the system noise) and maybe used to model non-stationarities in the echo path

R_(V)(k,f) corresponds to R₂(t) and estimates the variance additivemeasurement noise

A fundamental problem with acoustic echo cancellation is to allowre-adaptation in situations where the echo path has changed and retaingood echo attenuation in situations with a strong noise component i.e.double-talk conditions.

A common solution to the problem of discriminating double talk from echopath changes is to use a dual-filter structure. This incorporates havingtwo adaptive filters where the filter coefficients are transferred fromone filter to the other when either filter is performing significantlybetter than the other. These schemes pose several problem of determiningwhen either filter is performing significantly better, and also thetransfer of filter coefficients may not be suitable when the lengths ofthe respective filters differs significantly.

SUMMARY

An object of the present invention is to provide an improved echocanceller.

According to a first aspect of the present invention a method forestimating a model of an echo signal in an echo canceller is provided.The echo canceller comprises at least an adaptive main filter formodeling the echo signal and an adaptive shadow filter for modeling theecho signal. The adaptive main filter is an adaptive filter for whichthe adaptation speed is proportional to a system noise estimate and theadaptation speed of the shadow filter is faster than the adaptationspeed of the adaptive main filter. In the method, it is determinedwhether the adaptive shadow filter models the echo signal better thanthe adaptive main filter. If the adaptive shadow filter models the echosignal better than the adaptive main filter, the system noise estimateof the adaptive main filter is updated.

According to a second aspect of the present invention, an echo cancellerfor estimating a model of an echo signal is provided. The echo cancellercomprises at least an adaptive main filter for modeling the echo signaland an adaptive shadow filter for modeling the echo signal. The adaptivemain filter is an adaptive filter for which the adaptation speed isproportional to a system noise estimate and the adaptation speed of theshadow filter is faster than the adaptation speed of the adaptive mainfilter. The echo canceller comprises a processor for determining whetherthe adaptive shadow filter models the echo signal better than theadaptive main filter and an updater for updating the system noiseestimate of the adaptive main filter if the adaptive shadow filtermodels the echo signal better than the adaptive main filter.

An advantage with embodiments of the present invention is that a robustand fast solution for discriminating between double talk and echo pathchanges for echo canceller is provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates linear filters in echo cancellation according toprior art.

FIG. 2 is a flowchart of the method according to embodiments of thepresent invention.

FIG. 3 illustrates an echo canceller according to embodiments of thepresent invention.

FIG. 4 exemplifies an implementation of the echo canceller according toembodiments of the present invention.

DETAILED DESCRIPTION

The present invention will be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. The invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. In thedrawings, like reference signs refer to like elements.

Moreover, those skilled in the art will appreciate that the means andfunctions explained herein below may be implemented using softwarefunctioning in conjunction with a programmed microprocessor or generalpurpose computer, and/or using an application specific integratedcircuit (ASIC). It will also be appreciated that while the currentinvention is primarily described in the form of methods and devices, theinvention may also be embodied in a computer program product as well asa system comprising a computer processor and a memory coupled to theprocessor, wherein the memory is encoded with one or more programs thatmay perform the functions disclosed herein.

The present invention relates to updating of the adaptive filters usedin echo cancellers to improve the estimation of a model of the echosignal.

The basic concept of the invention is to extract information from anadaptive shadow filter to an adaptive main filter if the adaptive shadowfilter models the echo better than the adaptive main filter. Theadaptive main filter is exemplified by a Kalman filter in thedescription below, but it should it be noted that other filters typesalso may be used such as other types of Kalman filter realizations,Infinite Impulse Response filter, non-linear filters. It should also beunderstood that the adaptive main filter is also referred to as a mainfilter and the adaptive shadow filter is also referred to as a shadowfilter.

According to embodiments of the present invention an echo canceller 300for estimating a model of an echo signal is provided as illustrated inFIG. 3. The echo canceller 300 comprises at least an adaptive mainfilter 301 for modeling the echo signal and an adaptive shadow filter302 for modeling the echo signal, wherein the adaptive main filter is anadaptive filter for which the adaptation speed is proportional to asystem noise estimate and the adaptation speed of the shadow filter 302is faster than the adaptation speed of the adaptive main filter 301.

As shown in the flowchart of FIG. 2 and in conjunction with FIG. 3, itis determined 201 by a processor 303 whether the adaptive shadow filter302 models the echo signal better than the adaptive main filter. If theadaptive shadow filter models 203 the echo signal better than theadaptive main filter the system noise estimate of the adaptive mainfilter is updated 205 by an updater 304.

According to an embodiment of the present invention, the estimate of thesystem noise of the adaptive main filter is updated using a component Qwhich is dependent whether the adaptive shadow filter models the echosignal better than the adaptive main filter.

If the main filter models 203 the echo signal better than the shadowfilter the main filter is maintained updated and R_(H)(k,f) is also keptupdated. However, Q is set 206 to zero or not taken into account whenupdating the R_(H)(k,f). In this case it is being assumed that thesystem noise is zero and the updating of the main filter coefficientswould result in coefficients that better approximate the echo path andR_(H)(k,f) should be decreased.

Further, the degree of freedom, i.e. the number of free parameters, inthe shadow filter may be lower than, or equal to, the degree of freedomof the adaptive main filter. Free parameters imply parameters that canbe changed independently of each other. Thus, the time needed forestimating the free parameters increases with the number of freeparameters. Therefore a model using less free parameters (i.e. lowerdegree of freedom) can follow changes in the system faster than a modelusing more free parameters (i.e. higher degree of freedom).

According to one embodiment of the present invention it is determined202 by the processor 303 that the adaptive shadow filter 302 models theecho signal better than the adaptive main filter 301 if a residualsignal from the adaptive shadow filter 302 is lower than a residualsignal from the adaptive main filter 301. The residual signal e(t) isthe difference between the microphone signal y(t) and the estimated echosignal. When determining if the residual signal from the adaptive shadowfilter 302 is lower than the residual signal from the adaptive mainfilter, a security margin may be used. An example of how the securitymargin is determined is shown below:

$\begin{pmatrix}{{\sum\limits_{k}\; {{e\_ main}{\_ filter}\left( {t - k} \right)^{2}}},} \\{{k = 0},\ldots \mspace{14mu},{M - 1}}\end{pmatrix} > \left( {T \times {\sum\limits_{k}\; {{shadow\_ filter}\left( {{e(t)}^{2},{k = 0},{{\ldots \mspace{14mu} M} - 1}} \right)}}} \right.$

where T is the security margin.

Thus the embodiments of the present invention describes a method tochoose a time varying Q(k,f) in order to obtain a good trade-off betweenadapting the linear filter in situations when the echo path has changed,and keeping a good echo attenuation in situation when the power of thenear end signal, the additive measurement noise, is increased.

As mentioned above, the shadow filter is continuously updated with astep size larger than or equal to the step size of the main filter, i.e.the adaption speed of the shadow filter is faster than the adaptionspeed of the main filter, e.g. using

${\alpha (k)} = \frac{\mu}{R_{X}(k)}$

where μ≈1.

Preferably in case of a FIR filter, the length of the shadow filtershould be shorter than the length of the main filter in order to furtherincrease the update speed but also to reduce the memory requirements andthe computational complexity.

Due to the constantly high adaptation gain, the shadow filter willconverge more rapidly than the main filter in a situation with a changein the echo path, resulting in that the spectrum of the residual signalfrom the shadow filter is lower than that of the main filter,

R _(E) ^(s)(k,f)=|E ^(s)(k,f| ² <|E ^(m)(k,f| ² =R _(E) ^(m)(k,f)

In a situation with double talk, the two spectra will be of similarpower.

This information may be utilized to determine a suitable value ofQ(k,f).

Once the filter has converged, the stability against double talk isinherent in the adaptive step size frequency domain NLMS methoddescribed above, due to its construction: R_(H)(k,f) has converged to asmall value and that results in a small update gain also in situationswith increased power in E(k,f). Hence, with the choice Q(k,f)=0 orsufficiently small, R_(H)(k,f) would be kept at a small value and thelinear filter would stay in a converged state also during double talk.

This would, however, prevent the filter from being adapted to a new echopath if the echo path changes e.g. due to sudden movements in the echopath, which is also manifested by an increased power in E(k,f).

In order to re-adapt the filter, R_(H)(k,f) has to be able to increase.This can either be performed by a direct modification of R_(H)(k,f) orby choosing Q(k,f) sufficiently large so as to increase the adaptationspeed. This is in a direct conflict with the case of preventing anerroneous update in double talk conditions, as described above.

Several methods for the choice of Q(k,f) have been proposed in priorart. A common choice is to make Q(k,f) proportional to the power of themagnitude response of the estimated filter,

Q(k,f)∝Σ|H _(p)(k,f)|² =ERL

With steady state conditions on the signal powers, this leads to that

$\left. {R_{H}\left( {k,f} \right)} \right) \propto {{ERL} \cdot \left( {1 + \sqrt{1 + \frac{\left. {R_{V}\left( {k,f} \right)} \right)}{\left. {\beta \cdot {ERL} \cdot {R_{X}\left( {k,f} \right)}} \right)}}} \right)}$

Thus, with this choice of Q(k,f), the residual echo will be influencedby the absolute value of H(k,f) and the powers of the near end signalv(t) and the loudspeaker signal x(t). This indicates that the echosuppression will be slightly degraded during double talk, and that thefilter has to re-adapt when the double talk ends.

If the statistics of the change of the echo path was known, Q(k,f) maybe set to a small value when the echo path is stable thus preventingdivergence during double talk and to a larger value when a change in theecho path has occurred. This would however require some detectionalgorithm to determine when a change in the echo path has occurred.

Regarding the determination of Q, the parameter R_(H)(k,f) shall reflectthe averaged squared difference |H_(m)(k,f)−H₀(k,f)|² where H₀(k,f)denotes the filter coefficients of a linear filter modeling the echo.When a significant change in the echo path has occurred,|H_(m)(k,f)−H₀(k,f)|² may be larger than R_(H)(k,f). In such asituation, R_(H)(k,f) should be updated towards |H_(m)(k,f)−H₀(k,f)|² inorder to increase the adaptation speed, which is performed by settingQ(k,f) to a suitable value greater than zero.

As discussed above, in the case of a change in the echo path, the shadowfilter will start converging faster than the main filter. Thus, theestimation error of the shadow filter will be smaller than theestimation error of the main filter,

|H _(s)(k,f)−H ₀(k,f)|² <H _(m)(k,f)−H ₀(k,f)|²

and

|H _(m)(k,f)−H _(s)(k,f)|²<2·|H _(m)(k,f)−H ₀(k,f)|²

Thus, |H_(m)(k,f)−H_(s)(k,f)|² can be seen as an estimate of|H_(m)(k,f)−H₀(k,f)|² and is bounded upwards.

According to an embodiment of the present invention, a suitable choiceof Q(k,f) is estimated based on the difference of the filter estimate ofthe shadow filter and the main filter:

ΔH(k,f)=ρ·ΔH(k−1,f)+(1−ρ)·(H _(m)(k−1,f)−H _(s)(k−1,f))

Q(k,f)∝(ΔH(k,f))², if R _(E) ^(s)(k,f)<R _(E) ^(m)(k,f)

The factor ρ is included to perform an averaging of the difference ofthe two filter estimates. This limits the effects of a larger estimationerror in the shadow filter due to the larger step size. Typically, ρ isin the order of 0.75.

A well-known problem for adaptive algorithms is exponential growth ofthe covariance matrix if the input signal is not persistently exciting.For the Kalman filter, this translates to a risk for an exponentialgrowth of P(t) with the addition of R₁(t) if the input signal is notpersistently exciting. In the method according to the embodiments of thepresent invention, an exponential growth of R_(H)(k,f) would occur if atoo large Q(k,f) is added for a longer period when the input signal atthe corresponding frequency is low (or zero). To alleviate the problemof the exponential growth, Q(k,f) may be scaled by

${\gamma \left( {k,f} \right)} = \frac{R_{X}\left( {k,f} \right)}{\delta + {{R_{X}\left( {k,f} \right)}}_{\infty}}$

Thus, with a suitable choice of δ the factor γ(k,f) will be close tozero for low level signals.

In order to limit the effect of any remaining slow exponential growth,the term proportional to (ΔH(k,f))² is saturated as

max└(ΔH(k,f))²−R_(H)(k−1,f),0┘

This will limit the update R_(H)(k,f) to (ΔH(k,f))² times a constant.

When scaling is used, the following expression for the choice of Q(k,f)can be added to the expression for the update of R_(H)(k,f) using anoptimal step size.

Δ H(k, f) = ρ ⋅ Δ H(k − 1, f) + (1 − ρ) ⋅ (H_(m)(k − 1, f) − H_(s)(k − 1, f))${Q\left( {k,f} \right)} = \left\{ \begin{matrix}{\beta \cdot {\max \left\lbrack {{\left( {\Delta \; {H\left( {k,f} \right)}} \right)^{2} - {R_{H}\left( {{k - 1},f} \right)}},0} \right\rbrack} \cdot \frac{R_{X}\left( {k,f} \right)}{\delta + {{R_{X}\left( {k,f} \right)}}_{\infty}}} & {{{if}\mspace{14mu} {R_{E}^{s}\left( {k,f} \right)}} < {R_{E}^{m}\left( {k,f} \right)}} \\{0,} & {otherwise}\end{matrix} \right.$

The factor β is in the order of 0.25.

It should be noted that a similar expression also may be derived for thetime domain NLMS with optimal step size.

Accordingly as illustrated in FIG. 2 in conjunction with FIG. 3, theecho canceller comprises a scaling unit 305 for scaling 204 thecomponent Q before updating the adaptive main filter 301, where thescaling unit 305 may be configured to scale the component Q based on adifference between the corresponding filter parameters in the adaptivemain filter 301 and the shadow filter 302.

The functionalities within the box 350 of the echo canceller of FIG. 3can be implemented by a processor 801 connected to a memory 803 storingsoftware code portions 802 as illustrated in FIG. 4. The processor runsthe software code portions to achieve the functionalities of the echocanceller according to embodiments of the present invention.

The present invention is not limited to the above-described preferredembodiments. Various alternatives, modifications and equivalents may beused. Therefore, the above embodiments should not be taken as limitingthe scope of the invention, which is defined by the appending claims.

1. A method for estimating a model of an echo signal in an echocanceller comprising at least an adaptive main filter for modeling theecho signal and an adaptive shadow filter for modeling the echo signal,wherein the adaptive main filter is an adaptive filter for which theadaptation speed is proportional to a system noise estimate of theadaptive main filter and the adaptation speed of the shadow filter isfaster than the adaptation speed of the adaptive main filter, the methodcomprising: determining whether the adaptive shadow filter models theecho signal better than the adaptive main filter, and updating thesystem noise estimate of the adaptive main filter in response todetermining that the adaptive shadow filter models the echo signalbetter than the adaptive main filter.
 2. The method according to claim1, wherein the estimate of the system noise of the adaptive main filteris updated using a component Q which is dependent on whether theadaptive shadow filter models the echo signal better than the adaptivemain filter.
 3. The method according to claim 1, wherein the degree offreedom of the shadow filter is lower than, or equal to, the degree offreedom of the adaptive main filter.
 4. The method according to claim 1,wherein the step of determining whether the adaptive shadow filtermodels the echo signal better than the adaptive main filter comprises:determining a residual signal from the adaptive shadow filter is lowerthan a residual signal from the adaptive main filter.
 5. The methodaccording to claim 4, wherein the step of determining if a residualsignal from the adaptive shadow filter is lower than a residual signalfrom the adaptive main filter comprises using a security margin.
 6. Themethod according to claim 2, comprising the further step of: scaling thecomponent Q before updating the adaptive main filter.
 7. The methodaccording to claim 6, where the scaling of the component Q is performedbased on a difference between the corresponding filter parameters in theadaptive main filter and the shadow filter.
 8. An echo canceller forestimating a model of an echo signal comprising at least an adaptivemain filter for modeling the echo signal and an adaptive shadow filterfor modeling the echo signal, wherein the adaptive main filter is anadaptive filter for which the adaptation speed is proportional to asystem noise estimate and the adaptation speed of the shadow filter isfaster than the adaptation speed of the adaptive main filter, the echocanceller comprising: a processor for (i) determining whether theadaptive shadow filter models the echo signal better than the adaptivemain filter and (ii) updating the system noise estimate of the adaptivemain filter in response to a determination that the adaptive shadowfilter models the echo signal better than the adaptive main filter. 9.The echo canceller according to claim 8, wherein the processor isconfigured to update the estimate of the system noise of the adaptivemain filter using a component Q which is dependent on whether theadaptive shadow filter models the echo signal better than the adaptivemain filter.
 10. The echo canceller according to claim 8, wherein thedegree of freedom of the shadow filter is lower than, or equal to, thedegree of freedom of the adaptive main filter.
 11. The echo cancelleraccording to claim 8, wherein the processor is configured to determinewhether the adaptive shadow filter models the echo signal better thanthe adaptive main filter by determining whether a residual signal fromthe adaptive shadow filter is lower than a residual signal from theadaptive main filter.
 12. The echo canceller according to claim 11,wherein the processor is configured to determine whether the residualsignal from the adaptive shadow filter is lower than the residual signalfrom the adaptive main filter using a security margin.
 13. The echocanceller according to claim 9, wherein the processor comprises ascaling unit for scaling the component Q before updating the adaptivemain filter.
 14. The echo canceller according to claim 13, where thescaling unit is configured to scale the component Q based on adifference between the corresponding filter parameters in the adaptivemain filter and the shadow filter.
 15. The echo canceller according toclaim 8, wherein the processor comprises one or more of: amicroprocessor and an application specific integrated circuit (ASIC).16. The echo canceller according to claim 8, wherein the processorcomprises a microprocessor, and the echo canceller further comprisessoftware for instructing the microprocessor to (i) determine whether theadaptive shadow filter models the echo signal better than the adaptivemain filter and (ii) update the system noise estimate of the adaptivemain filter in response to a determination that the adaptive shadowfilter models the echo signal better than the adaptive main filter.